Your next customer may never touch the journey you spent months improving. They may not visit your homepage, open your app, read your comparison page, scroll through your FAQ, or follow the flow your team fought to simplify. They may ask an AI agent to do the work for them.

“Find the best internet plan for my home.”
“Compare these insurance options.”
“Check whether I can return this and tell me what to do next.”

That’s useful for the customer and a little uncomfortable for CX.

For years, we’ve treated the owned journey as the center of customer experience: the website, app, chatbot, contact center, store, and account portal. That made sense when customers were doing the work themselves. AI agents change the front door. The customer may still make the final decision, but the research, comparison, filtering, scheduling, and preparation may happen somewhere your team doesn’t control.

That means the work underneath the journey starts to matter more than the journey itself: product truth, policy clarity, knowledge quality, recovery paths, handoffs, and permission rules. All the things customers usually have to fight through quietly will move much closer to the surface.

When the Customer Isn’t the One Clicking
Most CX work assumes the customer is present. We map what customers see, measure where they click, study where they abandon, improve the form, rewrite the help article, reduce the steps, and celebrate when completion improves. That work still matters. It just may not be where the decision starts.

When customers delegate a task to an AI agent, the experience becomes whatever the agent can understand, retrieve, compare, trust, and act on. The agent doesn’t care that your homepage feels warm and aspirational. It won’t admire the photography or follow your product story in the order marketing intended. It wants the truth.

What does this cost? Who is eligible? What’s included? What happens if I cancel? Where do I go when something breaks? If those answers are scattered, stale, inconsistent, or buried in a PDF from three product launches ago, the experience is already fragile. The customer may never know where it broke. They’ll only know the answer was wrong, the recommendation was confusing, or the next step created more work than it removed.

Then the company gets the call.

The Customer Has Been Doing the Cleanup
A human customer can work around messy systems. They can open five tabs, read three help pages, call support, compare notes with a friend, study the fine print, and make an educated guess. That doesn’t mean the experience worked. It means the customer did the cleanup.

An AI agent may expose the mess faster. It might pull from your policy page, a support article, a comparison site, a customer review, and an old product document. Then it may summarize the answer as if your company has one clear version of reality. Many don’t.

Take home internet. A customer asks an agent to compare providers based on speed, price, installation timing, equipment fees, promotional expiration, contract terms, and cancellation rules. The agent won’t move through the journey the way your UX team designed it. It’ll assemble the decision from whatever it can find and trust. One provider has the nicer website but vague fee language. Another has less polish but clear pricing, structured plan information, visible installation windows, and cancellation terms that don’t require a scavenger hunt.

The agent may favor the company with cleaner truth over the company with cleaner visuals.

Companies have spent years improving the surface of the experience while the operating layer stayed messy underneath. Product information conflicts. Policy pages fall out of date. Exceptions live in employees’ heads. Recovery depends on finding the one person who knows what to do.

AI doesn’t make those problems disappear. It moves them closer to the customer.

Start With One Real Customer Task
The answer isn’t to chase every new AI interface or try to control every outside recommendation. Start with one high-value customer task: buying a plan, filing a claim, changing service, returning a product, booking an appointment, or renewing a contract. Review the actual sources an agent could use, including product pages, help articles, policy documents, public reviews, comparison sites, internal knowledge, and customer communications. Mark every place where the agent would have to guess, reconcile conflicting information, or send the customer somewhere else.

Then check these three things:

1. Can the agent find the correct answer without guessing?
2. Can it compare prices, fees, eligibility rules, exclusions, timelines, and tradeoffs clearly?
3. Can it tell which source to trust when the website, chatbot, support team, and policy documents don’t agree?

The customer also needs to understand what the agent is about to do, what access they’re providing, and where approval is required. If the agent makes the wrong move, there must be a practical way to reverse it without three transfers, competing policy interpretations, and a ticket number nobody can find. Task completion alone is a weak standard. The customer should also be able to understand, approve, and recover from what the agent did.

The Seams Are Moving
I don’t think CX has lost control of the experience. I think the idea of control was always a little inflated.

Customers have always built their own version of the journey. They asked friends, searched forums, read reviews, compared competitors, and called twice to see if two employees gave the same answer. They took screenshots because they didn’t fully trust the company to remember what it promised. AI agents make that behavior faster and reduce the amount of work the customer has to do personally. The customer is still trying to accomplish something. The company is still responsible for whether its promise holds up. CX still has to see across the seams.

But the seams are moving. They now sit between AI agents, data sources, external summaries, permission models, internal policies, operating systems, and the customer’s final moment of trust. If your experience only works when a patient customer clicks through the right screens, reads everything in the intended order, catches the exception, understands the fine print, and contacts the correct department when something breaks, you may not have a dependable customer experience.

You may have a dependency on customer labor. AI agents will expose that quickly. CX leaders now have to determine what an agent needs to represent the company accurately, help the customer act with confidence, and recover when something goes wrong. AI may change where the journey begins. The company still owns whether its promise holds up.

Mark Levy is a customer experience and product executive, author, and advisor with more than 25 years of operating experience at Fortune 500 companies. He writes Decoding Customer Experience and is the author of The Psychology of CX 101. Connect with him on LinkedIn.

The post Your Next Customer May Not Visit Your Website appeared first on CustomerContactMindXchange.

Known as The Global Customer Experience Connoisseur, Natalie Beckerman is an internationally recognized titan in the world of customer experience.

Her new book, When Did You Stop Caring?  offers a seasoned analysis of customer service strategy triumphs and failures, as delivered by organizations large and small. Throughout, she refutes the lie that “caring” is soft and instead explains how when done right, represents a disciplined, profit-driven approach to customer service. In myriad ways, Natalie reminds leaders that they do business with people, not spreadsheets.

More importantly, she paves the way forward, outlining how to deliver better customer service outcomes. Discussing lived use cases from her own life and expansive career, she illustrates how empathy and efficiency can go hand in hand.

Diving deeper, Frost & Sullivan, The Transformational Growth Company, ® recently posed the following questions about When Did You Stop Caring? to Natalie. Read on for her insightful responses.

The Caring Recession

Frost & Sullivan:  Your new book, When Did You Stop Caring,? opens with a discussion about the “caring recession,” a key reason you wrote the book. Can you briefly discuss this concept and why addressing it is critical to better customer service?

Natalie Beckerman: No one just wakes up one day and stops caring. We’re experiencing a Caring Recession. We have moved way past what we used to refer to as a crisis. The Caring Recession is not some poor quarterly performance or simply a rude employee – it’s a sustained structural withdrawal of human care from organizations at scale.  You can recover from a crisis; a recession reshapes expectations forever. I called it a recession on purpose, because, like an economic one, it compounds overnight before anyone names it. Customer Service is where it’s felt first and worst because that is the front door of the whole enterprise.  Unless we give it a name, then we won’t be able to start reversing its effects. By naming it we can begin the journey to bring the human lens back in balance – and we can start to care on purpose again.

Culture and the Middle Layer

Q:  In Part 2, you discuss how important culture is to a company’s success. You zero in on the importance of the middle layer. Can you elaborate? Any insights to help middle managers caught between C-Suite directives and challenged customer service teams?

A: Culture is not created by the executive level, nor is it lived at the top. It is passed through the middle. The C-suite creates the policy, but middle management will determine whether the policy is actually implemented. Therefore, I always call culture a base or foundation, not a sign or a poster. Middle Management sits on the fault line: executives sending orders down from the top, overworked staff members looking up, and everyone assumes middle management will simply take the heat off. We need to stop using middle management as a “relaying” point where pressure flows downward from upper management and begin viewing middle management as the ones who can provide air coverage to do the right thing.  A successful middle manager translates for their team, determines how policy affects the customer in front of him or her, and provides support so their employees can make the correct decisions.

Operationalizing the Mission

Q:  Operationalizing the enterprise mission statement (rather than just messaging around it) through the actual policies, procedures and actions of employees is another key concept in your book. How do you put this into practice? Any guidelines on how to make your company’s philosophy a lived reality for customers?

A: The space between a business’s claimed beliefs and the real-life experience of a customer is called the “Caring Gap.” Typically, the Caring Gap is filled with good intention and poor policy. An internal statement regarding a company’s belief system or their mission communicated as a message to customers is an example of a mission statement; however, when that same mission statement is embedded into a company’s operational practices, it becomes an example of a mission being lived.

To close the Caring Gap, I suggest that company leaders review every policy they have created and ask themselves if that particular policy supports their employees’ ability to provide better service to customers. Does it create barriers for their employees? Next, identify the times when team members are forced to make a decision regarding whether they should follow procedure, or do what is right for your customer at that moment, because during those moments your team members are testing your stated mission and discovering what you truly believe. Provide your team with the authority to use their own discretion in making decisions for customers. This is the Human Standard. It is not a marketing slogan that you can merely announce; rather, it is a measurement standard that you must live up to in all your operational activities.

The Business Case for Caring

Q:  In Part 4 you present the “business case for caring” — why is this important? How does caring affect return on investment? How does automation factor in?

A: Caring is not soft. It is economics. I wish more finance teams understood that. Loyal customers are cheaper to retain than to get back, they spend greater money over time and forgive you when you stumble – those are return on investment (ROI) statistics and sentiment.

Sheila (the name of my car) that was returned, made me a loyal customer of a brand for years because that brand didn’t argue with me about fixing my issue. Instead, they gave me their standard service which went the extra mile.

The turnover you avoid when your employees feel valued is ROI too. Every person who stays is one hire you didn’t have to make twice.

Automation is not the villain here. It’s indifference. Used properly, automation removes friction and drudgery so your people can be free for the moments which truly require a human — complicated, emotional, or the kind that people will remember. The failure is when we remove these very moments from our people by using automation to save small amounts of money.

Care is what compounds. Everything else is just cost management disguised as strategy.

Efficient versus Built to Last

Q:  Can you distinguish between a business that is very efficient/profitable versus one that is built to last? How does the organizational mindset differ? Any practical applications you can point to?

A: Efficiency is an important goal for companies; however, there is a difference between efficiency and being “built to last.” When organizations focus solely on being the most efficient, they may achieve short-term goals such as low costs or high profit margins. However, by focusing solely on how to lower their costs (i.e., eliminate non-essential jobs) while maintaining productivity, these organizations will ultimately fail.

Companies that have long-term goals tend to focus on building strong relationships with their customers, and therefore, the quality of service will be higher. Additionally, employees who work for companies that build long-lasting customer relationships are often rewarded, which also creates employee loyalty. As a result, employees become the greatest asset of a company. Trust also becomes an important component of a successful business. Building trust takes time and effort, but when achieved, it helps create long-lasting relationships with both employers and customers. Therefore, businesses that treat employees as assets and view trust as capital will be able to develop sustainable growth.

The Beckerman Caring Challenge™

Q:  Your book closes with “the Beckerman Caring Challenge” — a 12-month commitment to restore and reignite humanity. No small feat! Can you share a few key action steps with our readers?

A: A twelve-month plan seems overwhelming at first. The Beckerman Caring Challenge™ is a 12-month, 4-quarter roadmap to take your beliefs and transform them into tangible and observable leadership behaviors by putting your values into action.

You can begin wherever you’d like within the four quarters of the Challenge. Just get started. The Challenge was designed to create habits. Here are a couple of examples of the foundational elements I would consider starting with:

First, complete an objective assessment — identify your Caring Gap before announcing a single new project or program. You cannot address what you’re unwilling to acknowledge.

Second, establish a handful of commitments that will be non-negotiable, and then display them as such. Good intentions have a low success rate when it comes to actual completion. What holds people accountable to their commitments is “caring out loud.”

Third, schedule accountability on the calendar once each month — one intentional opportunity per month for leaders to ask themselves, “In which areas did we demonstrate caring? In which areas did we fail to care? What are we going to change?”

Accountability is what differentiates a value from simply being a feeling or vibe. And running underneath all of it is what I call the Flame Forward: the idea that reignited humanity isn’t something you keep, it’s something you pass on.  Do this for a year and you don’t just improve your metrics; you change what kind of company you are. And you bring the lenses of humanity and efficiency back in balance and back together.

Don’t Miss Natalie at Customer Contact West

Natalie Beckerman will be hosting a CX Women’s Leadership Alliance Workshop at the 22^nd Annual Customer Contact West: A Frost & Sullivan Executive MIndXchange in Tucson, Arizona from October 18^th to 21^s.t.  The event will take place at the JW Marriott Tucson Starr Pass Resort & Spa and the Women’s Workshop, themed Owning Your Growth Journey, will take place on Sunday, October 18^th at 2:15pm. Learn more here.

Described as a fixer in the customer experience and customer contact space, Natalie Beckerman has run top tier, large scale operations driving transformation and change, leading strategy, while conquering operational and service excellence across the Globe. She is known to build new business, increase revenue and turn around performance, while at the same time reenergizing and invigorating businesses and leaders. Natalie has done this in the telecommunications/mobile, Home Services, Hospitality and Fintech/Financial services industries.

The post When Did You Stop Caring? The Call to Reignite Humanity in a World Obsessed with Efficiency appeared first on CustomerContactMindXchange.

The real strategic advantage comes from transforming data into actionable insights that inform better decisions, build trust, and enable sustainable growth. Enterprise Customer Intelligence™ provides a framework for organizations to leverage customer understanding as a key strategic asset – positioning them ahead of competitors to secure long-term success.

This creates a paradox: despite abundant customer information, enterprise capabilities often remain fragmented. Insights are generated across various functions but are rarely integrated into a unified model that supports decision-making, governance, and trust.

The Need for Enterprise Customer Intelligence

Organizations need to move beyond isolated feedback programs and siloed data. The goal is to develop a comprehensive, real-time, integrated, and actionable understanding of customers. The Model provides a framework to achieve this by linking customer insights, decision-making, and governance to improve organizational performance and foster customer trust.

For CX and contact center leaders, the practical test is whether customer intelligence is being used to improve service design, reduce friction, and make faster, more informed decisions during moments that matter.

The Enterprise Customer Intelligence Transformation Model

This model converts customer signals into improved enterprise decisions. It underscores that customer trust and relationship health are vital indicators of whether these decisions strengthen relationships and create lasting value.

The model is built on four interconnected capabilities that progressively turn customer understanding into enterprise-wide intelligence, stronger trust, and sustainable success:

1. Build Unified Customer Understanding

Achieving a unified view of the customer requires transcending isolated surveys and feedback. Integrating Voice of Customer data, behavioral analytics, operational data, journey insights, and Customer 360 information provides a real-time, holistic picture of customer needs, expectations, and emerging friction points.

2. Align Intelligence with AI and Decision-Making

Customer intelligence adds value when it directly informs decisions. AI enhances this by processing large data volumes, identifying patterns, and generating predictive insights. Leading organizations leverage AI not only for customer service automation but also for strategic planning, risk detection, investment prioritization, and performance management. The strategic advantage lies in translating insights into actionable improvements.

3. Operationalize Intelligence Through Better Decisions

Embedding customer insights into decision processes transforms customer intelligence into an enterprise capability. Decision intelligence integrates understanding into functions, such as finance, operations, product development, marketing, customer service, and strategic planning. This approach enables organizations to evaluate trade-offs more effectively, prioritize initiatives, reduce friction, and align actions with customer needs and business objectives.

4. Strengthen Trust Through Governance

As AI and predictive models become central to operations, robust governance is essential. Trust-centered governance requires clear standards for transparency, privacy, ethics, and responsible AI. Demonstrating responsible data management and model use fosters customer trust and aligns organizational practices with ethical standards and customer expectations.

From Insight to Enterprise Value

To realize true value, customer intelligence must extend beyond insights teams and marketing functions. It must influence decision-making across the C-suite and throughout the organization. When integrated – combining understanding, AI, decision intelligence, and governance – organizations can:

• Reduce Enterprise Risk: Better identify friction points, compliance issues, emerging risks, and vulnerabilities early, reducing operational disruptions and reputational damage.
• Improve Enterprise Performance: Use customer insights to allocate resources more effectively, enhance efficiency, and prioritize high-impact initiatives.
• Strengthen Long-Term Value: Protect customer data, communicate transparently, and deliver experiences aligned with customer expectations – building loyalty, increasing lifetime value, and enhancing resilience and competitive positioning.

Strategic Implications

The next competitive advantage will not rely solely on data volume or AI sophistication. Instead, it depends on an organization’s ability to translate customer understanding into decisions that drive measurable outcomes. Enterprise Customer Intelligence – by integrating insight, AI, governance, and decision-making – shifts the focus from merely understanding customers to actively enhancing decisions, building trust, and creating long-term value.

For executive teams, the critical question is not how much data is available, but whether customer insights influence decisions that strengthen trust and relationships over time. Organizations that excel at this will differentiate themselves through stronger execution, more responsive decision-making, and enduring customer relationships.

By adopting Enterprise Customer Intelligence as a strategic capability, organizations can harness the full potential of their customer data – driving innovation, resilience, and leadership in their markets. Prioritizing this transformative methodology becomes essential for organizations seeking sustained competitive viability.

John Bord is a seasoned customer strategy executive, consultant, and thought leader with over 25 years of experience in Customer Experience (CX), Customer Intelligence, Voice of the Customer (VoC), strategic marketing and enterprise transformation. A Forrester Certified Customer Experience Professional, he is the author of the Enterprise Customer Intelligence™ Transformation Model and the Customer Trust & Relationship Health™ framework, which explore how organizations turn customer understanding into better decisions, stronger relationships, and sustainable enterprise value.

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Presented by DanaShapir Alviene, Chief Guest Officer, Breeze Airways

The post HEADLINER VIDEO: AI Without Fear: Building Trust in a Fast-Changing World appeared first on CustomerContactMindXchange.

The Event at a Glance
Customer Experience 2026 was structured around a central premise: that organizations have accumulated the right intentions, the right investments, and often the right data, but are still not seeing the outcomes those inputs should produce.

Sessions covered:

• Reframing CX measurement from performance reporting to operational steering
• Building executive buy-in through proof of business outcomes rather than vision statements
• Designing AI implementation that strengthens rather than replaces human judgment
• Creating operating models built for continuous learning and repeatable execution
• Repositioning CX as a structural investment, not a departmental function

Core Theme #1: Metrics Are Instruments, Not Trophies
One of the most consistent themes across sessions was a pointed challenge to how organizations use CX measurement. The conversation pushed away from retrospective reporting toward a different purpose entirely: creating a steering system rather than a scoreboard.

When measurement becomes a performance signal rather than a diagnostic tool, teams begin chasing the number instead of serving the customer. Frontline leaders described dashboards that look healthy while customer friction accumulates in the background, scores that improve as feedback quality declines, and executive reviews celebrating metrics nobody on the floor believes reflect what is actually happening. The shift called for across multiple sessions was toward leading indicators, signals from actual customer interactions, and decisions that use measurement to guide resource allocation rather than validate past quarters.

Key insights and action items:

• Assign ownership to outcomes, not metrics. A metric without a single accountable owner becomes a shared responsibility that nobody feels any urgency to move.
• Audit for gamification. If your team understands how scores are calculated, they can optimize for the score rather than the experience. Build measurement systems that are harder to game than to improve.
• Replace performance theater with operational signals. The question measurement should answer is not ‘how did we do?’ but ‘what should we do differently?’

Core Theme #2: Win Buy-In with Proof, Not Vision
Drew Dornhecker of Audi of America delivered one of the event’s most grounded case histories, describing how a CX initiative that initially appeared to leadership as a cost with no measurable return became a program mandated across the entire dealer network.

Early proposals framed the initiative around customer satisfaction and brand alignment. Both were accurate. Neither moved the budget conversation. The shift came when Dornhecker’s team stopped leading with the vision and ran a controlled pilot designed to generate irrefutable business data. The results showed the initiative improved satisfaction scores while reducing operating costs by approximately $250,000 per store annually, driven by reduced staff turnover and improved service efficiency. Early adopters became internal advocates. Skeptics began requesting the program. What had been a difficult sell became a network-wide mandate.

Key insights and action items:

• Start with a pilot, not a proposal. A contained test with clear success metrics converts skeptics more reliably than any presentation.
• Translate CX outcomes into financial terms leadership already tracks. Retention rates, operating cost reductions, turnover impacts, and revenue per customer are more persuasive in budget conversations than satisfaction scores.
• Let early adopters carry the message. Peer advocacy from internal champions who have seen results moves organizations faster than top-down mandates alone.

Core Theme #3: The 87/13 Problem in CX Investment
A finding from my own Capstone that generated significant discussion across several sessions: 87% of the variance in CX maturity is explained by three structural dimensions: organizational governance, culture, and strategy alignment. The remaining 13% is explained by the capabilities that tend to receive the most concentrated investment: listening programs, analysis tools, and action platforms.

Most organizations are concentrating resources at exactly the layer of CX that explains the least variance in outcomes. They build listening infrastructure while governance remains informal. They invest in analytics platforms while strategy stays disconnected from business goals. They launch action programs while accountability is diffuse across teams that were never designed to share ownership.

My session organized this finding using the metaphor of a climbing expedition. The tools and equipment matter, but what determines whether the team reaches the summit is whether they are acclimated to the environment, whether their route reflects real conditions, and whether the people responsible for decisions are genuinely aligned on where they are going. Attendees used a six-capability framework across culture, voice of the customer, journey management, customer health, intelligence integration, and governance to identify where their organizations were concentrating effort relative to where the structural gaps actually existed.

Key insights and action items:

• Audit where CX investment is concentrated. If the majority of budget and effort flows to listening and analysis, the 87% of the problem may be going unaddressed.
• Assess structural dimensions directly. Ask explicitly: Is CX strategy aligned to business goals? Do leaders share a definition of customer truth? Does governance exist to sustain execution across leadership transitions?
• Treat culture as a designed capability. Organizations that improve CX maturity build cultural expectations into how decisions are made and how teams are held accountable, not into posters and value statements.

Core Theme #4: AI Works Best as a Force Multiplier
AI was present in nearly every session, but the event’s collective position on implementation was more grounded than the broader market conversation. Practitioners across automotive, healthcare, and financial services described deployments that had produced measurable efficiency gains, and others that had created new operational problems, including increased escalation volumes and frontline teams busier managing AI outputs than they had been managing the original workload.

The organizations that described successful deployments were precise about which work AI should handle and why. Transactions, routing decisions, knowledge retrieval, and pattern recognition at scale were cited consistently as strong use cases. Complex problem-solving, situations requiring emotional judgment, and relationship-critical conversations remained areas where human presence was irreplaceable. A framing that resonated across several discussions positioned AI as a mentor rather than a monitor: a system that surfaces relevant knowledge and accelerates agent learning rather than one that scores behavior after the fact.

Key insights and action items:

• Define the use case before selecting the tool. AI implementations that fail most often do so because the problem was not defined precisely enough before the solution was chosen.
• Maintain human accountability for AI-enabled outcomes. The most effective deployments keep humans responsible for results, with AI improving how humans accomplish those results.
• Build governance into the deployment architecture. Monitoring, tuning, and review cadence should be designed before go-live, not as a response to problems that surface months after launch.

Core Theme #5: The Operating Model That Sustains CX
The final major theme addressed something less visible than AI or measurement but identified by multiple speakers as the actual determinant of whether any CX initiative produces results that outlast the attention that launched it: the operating model underneath execution.

One session framed this using an architectural metaphor. Durable customer experience requires a structure, with one foundational capability supporting everything above it and four pillars translating that foundation into ongoing behavior. The foundation is continuous learning. The pillars are continuous improvement, voice of the customer, end-to-end metrics, and an integrated organization. A parallel perspective from the pharmaceutical industry reframed the same problem differently: CX gets relegated to the ‘nice-to-have’ tier because it presents itself as a departmental maturity project rather than a structural investment in commercial outcomes.

Key insights and action items:

• Identify what your CX function would lose if its current champion left tomorrow. If the answer is substantial, the governance structure is not yet institutional.
• Design continuous learning as an operational capability, not an event. Documentation, standardized processes, and career pathways turn learning into organizational infrastructure rather than individual knowledge.
• Reframe the investment thesis. CX leaders who win sustained resources speak in terms of retention rates, acquisition cost, revenue per relationship, and risk mitigation, not satisfaction scores.

Looking Ahead
Customer Experience 2026: A Frost & Sullivan Executive MindXchange reflected a CX community that has moved past the foundational questions of whether customer experience matters and into a harder set of questions about how to build organizations that are genuinely better at it over time. The thread running through every major theme was consistent: intention is nearly universal in CX organizations today. Architecture is rare.

For CX leaders looking to continue this work alongside peers facing the same challenges, Frost & Sullivan’s Customer Contact West: A Frost & Sullivan Executive MindXchange takes place October 18^th to 21^st in Tucson, Arizona. Learn more here.

Sean Albertson is a customer intelligence strategist, keynote speaker, and author of 4ROCKS and Alignment on the Rocks. He presented ‘The Climb: The Enterprise Operating System Behind Resilient Customer Experience’ at Customer Experience 2026. He can be reached at sean@cx4rocks.com.

The post Customer Experience 2026: A Frost & Sullivan Executive MindXchange – Building the Architecture for Better Customer Service appeared first on CustomerContactMindXchange.

Presented by Dan Gamber
Director of Experiences
Bridgestone Retail Operations, LLC

The post Raising the Bar: Delivering Preemptive Customer Service appeared first on CustomerContactMindXchange.

This is obviously a hypothetical scenario – there is no such thing as a technically flawless platform! But it is a case study that, unfortunately for the customer, happens far too frequently in the real world.

What did Company A miss? They forgot about the customer. No customer ever said, “I hope they used fancy AI and cool automation technology to build their products”. Every customer does have the expectation that the products you build will reduce cognitive load, respect their time, and solve their problem quickly and easily.

Building truly customer-centric products requires moving past “what do users want” to asking “why do the users need it”. Here is a quick breakdown of some best practices as we unpack this philosophy:

• Don’t start with technology. Don’t begin by making important decisions like the kind of agentic framework to use, the target architecture, or whether to buy or build. These should come much later, after you have understood the problem. Instead, start by walking a few miles in the customer’s shoes.
  1. Look at where the customer is failing across the entire journey from the time they enter the funnel, see how often and where they are dropping off.
  2. Identify new features/products, then create a value-effort grid of everything you have identified that will make it easy for you to understand what type of product you should be building and what features you need to prioritize.

• Metrics are important, but the right metrics are everything. Move from output to outcome metrics.
  1. Measure what the customer sees and feels. Example of a common red flag: Company A rolled out a feature that has 98% adoption but returns negative customer satisfaction. The metrics dashboard celebrated the adoption and never looked past that.
  2. Layer quantitative telemetry on top of qualitative data from user feedback surveys, focus groups etc, to get the full picture. Don’t be alarmed if the two do not always tell the same story. What customers think they want and how they actually behave is the “Say Do” gap. You can only close that gap by looking at both types of data together.

• Plan and design for iteration and feedback. Waiting six months for a feature to improve is not an option:
  1. Design modular architectures that allow you to isolate and work on small chunks of features.
  2. Deploy experimentation platforms. Use synthetic and historic data to automate customer experience evaluation dashboards so that you can test against baseline CX metrics even before it reaches the customer.
  3. Insert customers into your feedback loop in real time. Perhaps you have identified a critical point in the journey where customers are abandoning and you simply don’t know why. Automate a micro-survey at this point in the journey, something light and quick like “looks like you are leaving, anything we can do better”?
  4. If you are using Agentic frameworks, enable real-time agent evaluations that supply dynamic feedback and automatically update your product roadmap every few minutes.

Building products that are relevant to the customer is the most reliable way to drive growth, increase revenue and improve satisfaction. A solution that solves a customer problem has much higher premium than the technology behind it. When the focus is on the customer, the product is a true market differentiator. Anyone can build products, but those who are customer–centric build lasting products that continue to evolve and meet the customer where they want to be met.

Shri has built her career at the intersection of customer experience, digital transformation, product strategy, and enterprise technology, helping organizations modernize how they serve customers, scale digital businesses, and prepare for the next era of AI-enabled engagement.

Across 20+ years in telecommunications, healthcare, financial services, insurance, and technology-driven environments, she has led transformation through ambiguity, complexity, and rapid change. Her work often begins where mandates are unclear, legacy systems limit progress, data ownership is fragmented, and teams need a stronger operating model to move from vision to measurable execution.

The post Building Products that Matter to the Customer appeared first on CustomerContactMindXchange.

The challenge is no longer only operational. It is energetic.

Every customer contact organization has systems, metrics, workflows, and technology. But beneath every escalation, service failure, agent interaction, and leadership decision is another layer that often goes unseen: the internal state from which people are operating.

That is where the Metacognitive Energy Method™ becomes valuable.

Why Energy Matters in Customer Contact Leadership

In high-pressure environments, leaders and agents do not simply respond to facts. They respond through their current level of stress, awareness, interpretation, and emotional regulation.

A leader under pressure may become reactive, overly controlling, avoidant, or disconnected from the people they are trying to support. An agent in a depleted state may struggle to show empathy, problem-solve creatively, or recover after a difficult interaction. A team operating in chronic stress may appear busy, but is not necessarily effective.

The Metacognitive Energy Method™ helps leaders pause and examine not only what is happening, but how they are perceiving and responding to what is happening.

This matters because customer contact work is emotional work. Customers often arrive frustrated, confused, anxious, or disappointed. Agents absorb that emotional load throughout the day. Leaders then absorb the pressure of performance metrics, staffing constraints, technology changes, and executive expectations.

Without a framework for managing internal energy, organizations risk solving surface-level problems while missing the deeper human operating system underneath them.

A Practical Framework for Better Leadership Response

The Metacognitive Energy Method™ helps leaders build awareness in four core areas:

1. Awareness: What is happening in the moment, internally and externally?
2. Becoming: Who am I choosing to be as a leader in this interaction?
3. Containment: Can I hold pressure without immediately reacting, rescuing, blaming, or shutting down?
4. Discernment: What response best serves the customer, the employee, the business, and the long-term culture?

For customer contact leaders, this framework creates a practical bridge between emotional intelligence and operational execution.

It helps leaders move from reaction to response.

Instead of immediately asking, “How do we fix this metric?” leaders can also ask:

What is this metric revealing about the energy of the team?

Where are agents experiencing friction, depletion, or confusion?

Are we leading from clarity or urgency?

Are our AI and automation strategies supporting human judgment, or simply adding another layer of pressure?

What internal state do our leaders need to model for the organization to perform sustainably?

These questions do not replace traditional contact center strategy. They strengthen it.

The Human Side of AI Transformation

As AI becomes more embedded in customer contact operations, the human side of leadership becomes even more important, not less.

AI can summarize calls, automate workflows, identify trends, and support faster resolution. But AI cannot replace the need for calm judgment, emotional regulation, trust-building, coaching, and human connection.

In fact, the more technology accelerates the pace of work, the more leaders need the ability to regulate their own energy and help their teams do the same.

A successful AI strategy is not only about adoption. It is about integration.

Leaders must help teams understand what is changing, why it matters, how their roles are evolving, and where human value remains essential. That requires more than communication. It requires presence.

When leaders are grounded, they create psychological safety. When they are clear, they reduce resistance. When they are regulated, they help teams move through uncertainty without collapsing into fear or fatigue.

A New Leadership Advantage

The next frontier of customer contact leadership is not only smarter technology or better process design. It is the ability to lead human energy through complexity.

Customer contact leaders who understand this will be better equipped to:

• Reduce reactive decision-making
• Improve agent resilience and engagement
• Strengthen coaching conversations
• Navigate AI transformation with less fear and more trust
• Create calmer, more consistent customer experiences
• Build cultures where performance and well-being can coexist

The leaders who will stand out in this next era are not the ones who push harder. They are the ones who can regulate faster, discern more clearly, and lead from a grounded internal state even when the external environment is changing.

Customer contact has always been about people. The future will belong to leaders who remember that people do not perform at their best simply because the process is efficient. They perform at their best when their energy is understood, supported, and intentionally led.

Lauren Guidry is a global operations and customer experience executive with more than 18 years of experience leading teams through complexity, transformation, and high-pressure business environments across logistics, supply chain, and service organizations.

Recognizing the connection between internal regulation and external performance,she became an ICF-certified coach through the Institute for Professional Excellence in Coaching (iPEC) and developed the Metacognitive Energy Method™, a practical framework that helps leaders recognize when performance is beginning to degrade, regulate their internal state, and make more intentional decisions in real time.

The post The Missing Layer in Customer Contact Transformation: Metacognitive Energy appeared first on CustomerContactMindXchange.

Several years ago, struggling to handle call volumes due to understaffing resulting from COVID, we were faced with a few options. Add an IVR and try to divert callers, making it harder to get to an agent. Or, add a customer-led conversational AI voice assistant that would answer the call immediately and help callers resolve their concerns and make reservations.

Over the past few years, I have made these observations in comparing the roadblocks of a traditional IVR to our new conversational assistant and how it became a pathway to help our callers and drive more revenue while reducing costs.

In the ever-evolving landscape of customer service, the way companies interact with their customers can make or break their reputation. Traditional Interactive Voice Response (IVR) systems have long been criticized for their tendency to act as roadblocks, frustrating callers with confusing menus and limited options. However, the emergence of Conversational AI assistants presents a paradigm shift, transforming customer interactions into seamless pathways towards issue resolution.

The IVR Roadblock

IVR systems have been a staple of customer service for decades, designed to automate call routing and reduce the burden on human agents. However, they often fall short in delivering satisfactory customer experience. Callers are greeted with a series of robotic prompts, forcing them to navigate through a maze of menu options, often leading to dead ends or irrelevant departments. Frustration mounts as customers struggle to articulate their issue within the confines of pre-determined choices, leading to longer call times and decreased satisfaction.

The Conversational AI Pathway

Conversational AI assistants represent a departure from the rigid structure of IVR systems, offering a more intuitive and natural way for customers to interact with businesses. Powered by advanced natural language processing (NLP) and machine learning algorithms, these virtual assistants can understand and respond to a wide range of inquiries, regardless of how they are phrased. By leveraging contextual understanding and personalized data, Conversational AI assistants guide callers along a pathway tailored to their needs, swiftly addressing their concerns and providing relevant solutions.

Key Differences:

1. Flexibility: IVR systems offer limited flexibility, constraining callers within predefined menu options. Conversational AI assistants, on the other hand, adapt to the unique needs and preferences of each caller, offering a more dynamic and personalized experience.
2. Engagement: IVR systems often leave callers feeling disconnected and frustrated due to their impersonal nature. Conversational AI assistants engage callers in natural, human-like conversations, fostering a sense of empathy and understanding.
3. Resolution Time: IVR systems can prolong resolution times as callers struggle to navigate through menus or wait for human assistance. Conversational AI assistants streamline the process by quickly identifying and addressing customer inquiries, leading to faster issue resolution and higher satisfaction rates.

The Path Forward

As businesses strive to enhance their customer service offerings, transitioning from IVR roadblocks to Conversational AI pathways represents a significant opportunity for improvement. By embracing the capabilities of Conversational AI technology, companies can elevate customer experience, drive operational efficiency, and build stronger relationships with their clientele. As customer expectations continue to evolve, the path forward is clear: it’s time to break down barriers and pave the way for seamless, conversational interactions.

With over 35 years of experience, Brian Jeppesen is a seasoned leader in contact center operations and two-time Frost & Sullivan award winner. He specializes in deploying Conversational AI to enhance customer experience and reduce costs, has led teams of up to 2,500 agents across global brands including Landry’s and Golden Nugget and serves on the Board of Directors for the Contact Center AI Association.

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